class05.R

# Class 05: Data Visualization
# install.packages("ggplot2")
library(ggplot2)

head(cars)

##   speed dist
## 1     4    2
## 2     4   10
## 3     7    4
## 4     7   22
## 5     8   16
## 6     9   10

# All ggplot have at lieast 3 layers,
# data + aes + geoms
p <- ggplot(data = cars) + 
  aes(x = speed, y = dist) +
  geom_point() +
  labs(title = "Stopping Distance of Old Cars", 
       x = "Speed (MPH)", 
       y = "Stopping Distance (ft)") + 
  geom_smooth(method = "lm", formula = 'y ~ x',  aes(x = speed, y = dist))

show(p)

# Side-note: ggplot is not the only graphics systems
# a very popular one is good old "base" R graphics
plot(cars)

# Plot some gene expression results.
# Dataset is online in tab separated format so we
# use the read.delim() function to import into R
url <- "https://bioboot.github.io/bimm143_S20/class-material/up_down_expression.txt"
genes <- read.delim(url)
head(genes)

##         Gene Condition1 Condition2      State
## 1      A4GNT -3.6808610 -3.4401355 unchanging
## 2       AAAS  4.5479580  4.3864126 unchanging
## 3      AASDH  3.7190695  3.4787276 unchanging
## 4       AATF  5.0784720  5.0151916 unchanging
## 5       AATK  0.4711421  0.5598642 unchanging
## 6 AB015752.4 -3.6808610 -3.5921390 unchanging

# Q. How many genes in this dataset
nrow(genes)

## [1] 5196

# Q. How many genes are "up"?
table(genes$State)

## 
##       down unchanging         up 
##         72       4997        127

# Q. What % are up?
round(table(genes$State)/nrow(genes) * 100, 2)

## 
##       down unchanging         up 
##       1.39      96.17       2.44

p <- ggplot(genes, aes(x = Condition1, y = Condition2, col = State)) + 
  geom_point()

show(p)

p <- p + scale_color_manual(values = c("blue", "grey", "red")) + 
  labs(title = "Gene Expression Changes Upon Drug Treatment", 
       x = "Control (no drug", 
       y = "Drug Treatment")

show(p)

# Let's explore the gapminder dataset
# install.packages("gapminder")
library(gapminder)
head(gapminder)

## # A tibble: 6 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

# Let's make a new plot of year vs lifeExp
p <- ggplot(gapminder, aes(x = year, y = lifeExp, col = continent)) +
      geom_jitter(width = 0.3, alpha = 0.4) +
      geom_violin(aes(group = year), alpha = 0.2, 
                  draw_quantiles = 0.5) 

show(p)

# Install the plotly
# install.packages("plotly")
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

ggplotly()